Linganisha mbinu

	Muhtasari wa Muktadha-Ubadilifu	Uhamishaji wa Kujifunza na Ufupishaji wa Maandishi
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2019–2021	2019–2020
Mwanzilishi	Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)	Raffel et al. (T5); Lewis et al. (BART)
Aina	Domain adaptation of sequence-to-sequence neural summarization	Transfer learning applied to sequence-to-sequence summarization
Chanzo asilia	Fabbri, A. R., KryŜiński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021). SummEval: Re-evaluating Summarization Evaluation. Transactions of the Association for Computational Linguistics, 9, 391–409.	Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
Majina mbadala	domain-adapted summarization, domain-specific summarization, cross-domain summarization, DA-summarization	pretrained summarization model, fine-tuned summarization, TL-summarization, neural abstractive summarization via transfer learning
Zinazohusiana	6	4
Muhtasari	Domain-adaptive text summarization fine-tunes or adapts a pre-trained sequence-to-sequence language model on a target domain corpus so that summaries conform to domain-specific vocabulary, style, and factual constraints. It bridges the gap between general-purpose summarization models trained on news or web data and specialized domains such as biomedical literature, legal documents, scientific papers, or financial reports.	Transfer Learning with Text Summarization adapts a large language model pre-trained on broad text corpora — such as T5, BART, or PEGASUS — to the task of condensing documents into shorter, coherent summaries. By reusing learned linguistic knowledge and fine-tuning on domain-specific pairs of source documents and reference summaries, this approach achieves strong summarization quality with modest labeled data requirements.